Search Results (206)

Due to the increasing heterogeneity of consumer needs and preferences, manufacturing companies are forced to expand their product range to maintain market share while avoiding cost increases. However, increasing product variety increases the complexity of assembly systems and complicates planning, design, and production management. The quantification of manufacturing complexity and its impact on key performance indicators remains a subject of debate. To examine the relationship between assembly complexity, assembly line balance, and productivity from an operator-oriented perspective, an empirical complexity indicator for mixed-model assembly workstations is proposed. This indicator is based on experimentally collected data and analysis of working time variability. The proposed indicator is evaluated through controlled experimental case studies conducted in a learning factory environment. The results indicate that the relationship between complexity and productivity is not linear. Instead, within the investigated experimental boundaries, the observed trend suggests a turning point beyond which further increases in complexity are associated with decreased productivity, while line balance continues to improve. This finding suggests that integrating the proposed complexity indicator into production planning and management may support decision-making related to assembly line balancing and complexity management in manual assembly systems. Full article

Across the world’s electrical substations, water-treatment plants, and manufacturing lines, a single engineer with valid credentials and a laptop can today push new control logic to a programmable logic controller (PLC) and change the physical behaviors of safety-critical equipment within minutes. Firmware and ladder-logic updates on SCADA and industrial IoT systems are privileged operations: an attacker installing a malicious update controls the physical process. Existing protections concentrate install authority in a single party with no externally verifiable record; compromise of the vendor key, the engineering workstation, or any operator credential suffices to deliver an attacker-chosen payload to a PLC. CertiFlash binds every update to four independent approvals: a vendor signature, a FROST-Ed25519 threshold signature from an operator quorum, a per-session nonce from the PLC, and a monotonic counter. Every decision is recorded in an append-only Merkle transparency log. The PLC verifies the aggregate with a standard RFC 8032 Ed25519 verifier, requiring no FROST-specific device code. Four security properties (authenticity, authorization, rollback resistance, auditability) are machine-checked in Tamarin under a Dolev–Yao adversary with up to t − 1 compromised operators and corroborated through ten attack scenarios. The implementation runs with concurrent Modbus TCP and Siemens S7 traffic, blocks all attacks, signs in 27–192 ms (k = 3–10), keeps ML-DSA-65 within 6% of Ed25519 from 1 KiB to 10 MiB, and sustains 30.1 updates/s on 100 PLCs. The operator-quorum signature remains FROST-Ed25519: the design is partially post-quantum in the evaluated version. The framework maps to IEC 62443-3-3 SR 3.4 and NIS2 Article 21(2)(d–e). Full article

High-Frequency Trading (HFT) dashboards require rapid reception, aggregation, and visualization of order book and trade update streams that may arrive at multi-million message rates. Conventional CPU-based and CPU-GPU hybrid visualization pipelines can suffer from significant delays during periods of burst due to CPU-mediated rendering, synchronization, kernel launch overhead, and copies on the host. This paper presents a visualization pipeline that is entirely resident on the graphics processor with zero-copy access to NIC accessible pinned buffers, persistent CUDA processing, fused stage execution of the parse-aggregate pipeline, and persistent CUDA OpenGL buffer interoperation. The goal is not to reach production status but rather to see whether host-to-host data movement can be decreased and whether the stages of GPU processing can be consolidated to improve latency, throughput and frame cadence in controlled HFT-style workloads. The evaluated workstation achieved a mean ingest-to-pixel latency of 6.3 ms using the proposed design compared to 29.4 ms for the current design, with sustained throughput of 10.2 million messages per second, which is 20 times greater than the current design, and a steady-state range of 185 to 192 frames per second with a burst floor of 178 frames per second for the proposed design. The improvement observed can be attributed to both the zero-copy ingestion and fused persistent kernel execution. Based on the obtained results, the proposed method of use of this technique in the implementation of real-time financial visualization under the proposed conditions is possible. More general testing is still required on other NICs, other generations of GPUs and PCIe configurations, workload traces, and actual exchange feeds. Full article

Designing an optimal digital pathology workspace is essential to ensure diagnostic accuracy and safeguard the long-term well-being of pathologists. While digital pathology improves reproducibility, facilitates multidisciplinary collaboration, and supports data-driven precision medicine, its clinical effectiveness depends not only on computational performance but also on the physical and ergonomic environment in which pathologists operate. Inadequate workstation design may impair visual perception, increase cognitive and musculoskeletal strain, and potentially affect diagnostic consistency. Moreover, the progressive integration of artificial intelligence (AI) into routine diagnostics introduces additional requirements related to display performance, visualization interfaces, and human–machine interaction. Despite the rapid global adoption of digital pathology systems, standardized recommendations addressing ergonomic, environmental, and technological aspects of the digital workspace remain limited. In this work, we propose a clinically oriented framework for the design of digital pathology workspaces suitable for AI-assisted diagnostics. Key elements include the selection and calibration of medical-grade displays, ergonomic furniture and input devices, optimized ambient lighting conditions, and institutional quality assurance procedures. Emerging developments, such as intelligent ergonomic monitoring, advanced visualization interfaces, and adaptive AI-assisted workflows, may further support safe, sustainable, and high-performance digital diagnostic environments. Full article

The rapid advancement of quantum computing poses a fundamental threat to classical public-key cryptographic systems, necessitating the transition to post-quantum cryptography (PQC). While significant progress has been made in the standardization of quantum-resistant algorithms, their practical deployment in heterogeneous environments—particularly resource-constrained Internet of Things (IoT) devices—remains a critical challenge. This study presents a comprehensive experimental evaluation of four NIST-standardized PQC algorithms: CRYSTALS-Kyber (ML-KEM), CRYSTALS-Dilithium (ML-DSA), FALCON, and SPHINCS+. The scope of these findings is bounded by an empirical analysis conducted across two specific testing platforms, a high-performance x86-64 workstation (AMD Ryzen 7 5700U) and a resource-constrained embedded microcontroller (ESP32-WROOM), utilizing dedicated software environments implemented in Native C, Go, and Python. The evaluation isolates key performance indicators, including computational latency, memory consumption, communication overhead, and temporal determinism, based on benchmarking over 1000 iterations. Within this experimental setup, results demonstrate clear trade-offs between target security categories, execution performance, and structural memory limits. Lattice-based schemes such as Kyber and Falcon exhibit optimal efficiency and scalability on the tested embedded platform, while the specific memory limits of the ESP32 platform introduce architectural stability constraints for higher-tier Dilithium variants. In contrast, SPHINCS+ provides structural robustness at the cost of higher computational hashing latency within these evaluation environments. The findings highlight the critical role of hardware-specific constraints and language runtime design choices in enabling practical PQC deployment, providing context-specific insights supporting the secure migration of IoT infrastructures toward quantum-resilient systems. Full article

Musculoskeletal disorders represent one of the most frequent occupational health problems in labour-intensive industries, particularly in fish processing, where repetitive tasks and prolonged postures are common. The objective was to determine the level of ergonomic risk by applying the Rapid Entire Body Assessment (REBA) method and based on the results, to formulate recommendations aimed at preventing musculoskeletal disorders and improving preventive management within the organization. The assessment included 30 workers distributed across three operational workstations, where the overall average REBA score was 8.60 ± 1.65 (range: 6–12), indicating a predominantly high level of ergonomic risk. In categorical terms, 60.0% of the workers were classified as high risk, 13.3% as very high risk, and 26.7% as medium risk, while none reached negligible or low risk levels. Significant differences were observed between workstations (Kruskal–Wallis H = 16.72, p < 0.001, ε² = 0.545), with the nobbing stage exhibiting the highest biomechanical load (mean REBA = 10.38 ± 1.06). It is concluded that ergonomic risk is structurally integrated into the operational design of the evaluated production system; therefore, ergonomic interventions focused on redesigning workstations, adjusting height, and configuring tasks are recommended to reduce biomechanical exposure and strengthen the organization’s preventive occupational safety framework. Full article

This study investigates the impact of selected automation scenarios on occupational safety, ergonomics, and operational performance in a plastic crate production workstation. The research focuses on a specific case from the discrete manufacturing sector and aims to develop an integrated analytical framework combining ergonomic assessment with process simulation for the evaluation of organizational and technological improvements in manual handling operations. This study applies a simulation-based production model developed in the DBR77 discrete-event simulation environment to analyze alternative workstation configurations. The assessment framework integrates Ishikawa analysis for root-cause identification and the RULA and REBA methods for ergonomic risk evaluation. The investigated workstation was characterized by repetitive manual handling activities, awkward working postures, and increased physical workload associated with palletizing and transport operations. Several organizational and technological variants were analyzed, including additional operator support, robot-assisted palletizing, conveyor integration, and automated guided vehicle (AGV) transport. The simulation results indicated that the AGV-supported configuration achieved the shortest cycle time (1270 s per batch of 30 units), whereas the robot-assisted variant resulted in the longest cycle time (1520 s). Ergonomic assessment showed a reduction in RULA scores from 6–7 to 3–4 and REBA scores from 8–10 to 4–5 in the automated scenarios. The contribution of this study lies in the integration of ergonomic risk assessment and discrete-event simulation within a unified evaluation framework for workstation redesign in discrete manufacturing environments. The findings demonstrate how simulation-supported analysis can support decision-making regarding the balance between manual labor and automation under specific operational conditions. Due to the single-case-study design, the results should be interpreted as context-specific and exploratory rather than directly generalizable to all manufacturing systems. Full article

Human–robot collaborative workstations are increasingly deployed in industry, yet their design mainly focuses on productivity and ergonomics, with limited consideration of worker empowerment. This paper proposes a framework integrating Enabling Collaborative Situations (ECSs) into the design and evaluation of collaborative assembly systems. Following an exploratory design-based case study approach, the framework is implemented through the development of a physical demonstrator workstation in a realistic assembly context. It structures task allocation and interaction design while introducing an operational ECS evaluation grid combining industrial performance, ergonomics, and worker experience. The results suggest that collaboration design can preserve expected industrial performance while expanding worker autonomy, supporting work activity, and providing a basis for more empowering human–robot collaborative workstations. Full article

Crop leaf disease segmentation in complex natural environments remains challenging because lesion regions often exhibit substantial scale variation, blurred boundaries, and severe background interference. To address these issues, this study proposes a Multi-Scale Feature Rectification Network (MFR-Net) for crop leaf disease segmentation. The proposed network adopts an EfficientNetV2-S-based encoder to extract hierarchical features, incorporates a hybrid attention mechanism to enhance lesion-sensitive spatial and channel representations, introduces a Cross-Window Atrous Spatial Pyramid Pooling (CWASPP) module to strengthen multi-scale contextual modeling, and employs a Feature Rectification Module (FRM) in the decoder to alleviate semantic inconsistency during cross-level feature fusion. Experiments on a Kaggle-derived benchmark constructed from the unaugmented data folder of the public Leaf Disease Segmentation Dataset, containing 588 diseased-leaf images and 588 corresponding binary lesion masks, showed that MFR-Net achieved the highest mIoU of 74.27% and the highest Recall of 87.61% among the compared methods, and maintained competitive Dice performance (84.25%) with 25.10 M parameters and 37.55 G FLOPs. Ablation results further confirmed the effectiveness of the proposed design, with CWASPP providing the most notable individual contribution. Additional experiments were conducted on an independent Apple Leaf Dataset comprising 3197 image–mask pairs, collected under mixed controlled and natural field-like imaging conditions. The results showed competitive performance under a different data distribution, and robustness evaluation further verified stable performance under severe noise, blur, darkness, and contrast variation. All experiments were implemented in PyTorch 2.11.0 (CUDA 12.8) on a workstation equipped with an NVIDIA GeForce RTX 4060 Ti GPU (8 GB). These results indicate that MFR-Net provides an effective and robust solution for crop leaf disease segmentation in complex agricultural scenarios. Full article

Drawing on mobile foot reflexology in Taiwan, this article examines innovation readiness in small-scale wellness services where formal R&D resources, standardized workstations, and organizational support systems are limited. It conceptualizes readiness as a staged service-design condition comprising problem-recognition readiness, practitioner-agency readiness, co-creation readiness, and implementation-fit readiness. The empirical design integrated workplace observation, a survey of 59 therapists, semi-structured interviews with 10 therapists, expert consultation with 7 specialists, and two rounds of prototype evaluation (n = 17 and n = 19). Rather than treating ergonomic symptoms as an isolated occupational health outcome, the analysis traces how discomfort, posture constraints, psychosocial resources, practitioner narratives, and expert judgment were translated into design parameters and two chair prototypes for mobile service delivery. Three cross-phase mechanisms emerged: constraint visibility, practitioner-mediated translation, and implementation-fit testing. Shoulder, wrist/hand, and low-back discomfort signaled unresolved operational friction; high meaning and competence scores pointed to a practitioner resource base for adaptive participation; and staged prototype testing identified portability, adjustability, stability, and bodily comfort as the central adoption conditions. The article contributes to Administrative Sciences by showing that grassroots service innovation readiness is not simply an attitudinal state but an enacted process through which field constraints are made visible, jointly interpreted, and converted into a deployable service-support solution. Beyond this case, the staged readiness logic may also inform mobile wellness, community-care, rehabilitation-support, personal-care, and other low-resource service organizations that must convert frontline constraints into feasible service-support interventions. Full article

This paper presents a Digital Twin (DT)-based framework for the control, monitoring, and intelligent optimization of an Assembly/Disassembly/Repair Mechatronic Production Line (A/D/R MPL), developed as a laboratory platform aligned with Industry/Education 4.0/5.0 paradigms. The A/D/R MPL is assisted by two complementary cyber–physical robotic systems: an Assembly/Disassembly/Replacement Cyber–Physical Robotic System (A/D/R CPRS), and a Mobile Cyber–Physical Robotic System (MCPRS), enabling both fixed and mobile intelligent operations. The CPRS is equipped with an industrial robotic manipulator (IRM) responsible for A/D/R tasks, while the A/D Mechatronic Line (A/D ML) consists of seven interconnected workstations (WS1–WS7) dedicated to storage, transport, quality control, and final product handling. MCPRS includes a wheeled mobile robot (WMR), carrying a robotic manipulator (RM) and Mobile Visual Servoing System (MVSS). Each workstation is connected to a local slave programmable logic controller (PLC), which communicates via PROFIBUS with a master PLC located at the CPRS level. Additional communication infrastructures include LAN PROFINET and LAN Ethernet for local integration, and WAN Ethernet connectivity enabled through open platform Communication-Unified Architecture (OPC-UA), ensuring interoperability, scalability, and remote accessibility. Also, MODBUS TCP as serial industrial communication is used between the master PLC and the MCPRS. Virtual environment supports task planning through Augmented Reality (AR) and real-time monitoring through Virtual Reality (VR). The system behaviour is modelled with synchronized hybrid Petri Nets (SHPNs) which describe the discrete and hybrid dynamics of A/D/R processes. Artificial intelligence (AI) techniques are integrated into the DT framework for optimal task scheduling and adaptive decision-making. As a laboratory-scale implementation, the proposed system provides a comprehensive platform for experimentation, validation, and education. It supports Education 4.0/5.0 objectives by facilitating hands-on learning, human–machine interaction, and the integration of emerging technologies such as AI, Digital Twins, AR/VR, and cyber–physical systems. At the same time, it embodies Industry 4.0/5.0 principles, including interoperability, decentralization, sustainability, robustness, and human-centric design. Full article

The optimal placement of robotic manipulators within industrial workstations is a critical problem that directly affects task feasibility, accessibility, and operational efficiency. Improper base positioning can lead to joint saturation, reduced manipulability, and limited workspace utilization. This work presents an optimization framework for determining the optimal base placement of robotic manipulators by maximizing a joint-centering performance index based on the κ-index, which quantifies the proximity of joint variables to their allowable limits. The proposed methodology integrates geometric accessibility constraints with a constrained optimization formulation to ensure feasible robot configurations within the workspace. Three optimization strategies—constrained nonlinear programming, gradient projection methods, and genetic algorithms—are evaluated and compared in terms of solution quality and computational performance. Numerical simulations are conducted using a planar 2-DOF manipulator to illustrate the proposed framework and to analyze the influence of workspace geometry on optimal base placement. Additionally, an industrial case study involving the ABB IRB 120 robotic manipulator is presented to assess the practical applicability of the proposed approach. The results demonstrate that the optimization framework improves joint distribution within the allowable limits and enhances robot accessibility across the task workspace. The proposed method provides a practical tool for intelligent workstation design and robotic cell layout optimization in modern industrial environments. Full article

To address the application requirements of energy storage devices for new energy electric vehicles—including high energy density, high-power density, fast charging and discharging, and long-term cycling stability—traditional symmetric supercapacitors are often limited by low energy density and poor compatibility between the anode and cathode, making it difficult to meet the high-efficiency energy storage demands under the dynamic operating conditions of electric vehicles. This study focuses on the regulation of hierarchical thin-film structures and the innovative heterogeneous coating interface engineering with precise slurry coating and film-forming optimization and designs and fabricates NiCo₂O₄/NiFeO composite thin-film electrodes and Mn-doped porous carbon (Mn-PC) thin-film electrodes. The uniform, compact and stable coating formation on nickel foam substrates via controllable slurry coating facilitates the efficient integration of active materials and conductive supports. The electrode slurries were coated onto conductive nickel foam substrates, and high-performance aqueous supercapacitors were assembled using an asymmetric configuration. A systematic study was conducted covering material preparation, structural characterization, electrochemical testing, and full-device performance evaluation. Using techniques such as XRD, XPS, SEM, TEM, BET, and an electrochemical workstation, the study revealed the structure–activity relationships among material morphology, crystalline phases, pore structure, and electrochemical performance, elucidating the charge storage mechanisms of the composite electrode films and the principles of synergistic adaptation between the anode and cathode. The results indicate that NiCo₂O₄ nanowires decorated with in situ-grown NiFeO nanosheets to form a composite structure; when coated onto nickel foam, this forms a uniform, porous electrode film with a specific surface area of 171.3 m²/g, a specific capacitance as high as 1746 F/g at 1 A/g, and a capacity retention rate of 94.0% after 10,000 cycles. After coating and film formation, the Mn-PC anode introduced pseudocapacitive active sites through uniform Mn doping, resulting in a film electrode specific capacitance of 348 F/g and significantly improved rate and cycling performance. The assembled NiCo₂O₄/NiFeO//Mn-PC asymmetric supercapacitor exhibits a thin-film electrode specific capacitance of 153 F/g at 1 A/g, with a maximum energy density of 52 Wh/kg. Even at a power density of 9000 W/kg, it maintains 45 Wh/kg, and retains 89.5% of its capacity after 10,000 cycles, with overall performance outperforming most previously reported transition metal-based devices. This coating-engineered electrode fabrication strategy breaks through the interface mismatch and structural instability bottlenecks of traditional thin-film electrodes, providing a novel material system and an efficient coating assembly strategy for high-performance supercapacitor thin-film electrodes in new energy electric vehicles, and offers experimental evidence and technical references for the development and application of high-power energy storage coating devices for automotive use, as well as the innovative design of electrode coating engineering in energy storage fields. Full article

The energy-intensive nature of primary aluminum production necessitates advanced computational tools for process optimization. This study presents a coupled electro-thermal model of an aluminum reduction cell, developed within the framework of smart manufacturing. Using the finite difference method (FDM) implemented in MATLAB R2025b, the model resolves the three-dimensional configuration of a cell with eight prebaked anodes across four distinct physical domains (electrolyte, anodes, cathode, and gas phase). The computational grid comprises approximately 45,000 nodes with refined vertical resolution (Δz = 0.025 m) in the interelectrode gap. The electrostatic solution converges within 150–200 iterations using successive over-relaxation (SOR, ω = 1.5), with a total runtime under 15 min for 30,000 s of simulated physical time on a standard desktop workstation. Simulation results reveal characteristic temperature profiles with maxima reaching 1150 °C and a thermal uniformity index of approximately 130 °C across the central cross-section. The predicted specific energy consumption of 14.0 MWh/t Al aligns with industrial benchmarks. This computationally accessible virtual testbed enables rapid assessment of design modifications and process parameters, supporting the goals of energy efficiency and enhanced operational stability in primary aluminum production. Full article

This paper addresses the use of discrete-event simulation as a tool for optimizing the production process of an assembly line and identifying the potential for improving production efficiency. A digital model of the manufacturing system was developed in the FlexSim simulation environment based on real production data, technological operation sequences, and statistically defined cycle times. The model was designed to accurately represent real production conditions, including control logic, resource interactions, and material flow. The simulation results were analyzed using graphical and quantitative reports, which enabled the identification of production bottlenecks and inefficient resource utilization. Based on the obtained data, a process optimization strategy was proposed in the form of intelligent operator rotation between workstations to increase operator utilization and improve overall system efficiency. The proposed modifications were subsequently verified through simulation experiments, confirming the preservation of the required production capacity while improving the efficiency of human resource utilization. The findings confirm that simulation modeling represents an effective tool for the analysis, design, and verification of optimization measures, enabling the reduction in operational costs and risks associated with implementing changes in real manufacturing environments. Full article